Section 154 · Chapter 18, Embodied and Long-Running AI Systems
Testing Swarms and Societies of AIs
When many AI agents collaborate, compete, delegate, and negotiate, quality emerges from the society, not just the individual agent.
unit testswarmswarms societies ais
What to do
- Test communication contracts.
- Test resource contention.
- Test emergent behavior with long runs.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for unit test, swarm, swarms societies ais needed to reproduce work on Testing Swarms and Societies of AIs.
- Report results for unit test, swarm, swarms societies ais by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, swarm testing should use multi-agent traces, graph analysis of communication, shared-memory audits, adversarial agents, incentive testing, cost caps, deadlock detection, consensus quality scoring, and long-horizon simulation.
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Cite this page
Jason Arbon. "Testing Swarms and Societies of AIs." Testing AI Knowledge Edition, section 154.
https://jarbon.ai/testing-ai/knowledge/ch154-swarms-societies-ais.html